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COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database
Daniel Korn1, Tesia Bobrowski2, Michael Li1
1Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Bioinformatics (Oxford, England)
|November 11, 2020
Summary
COVID-KOP integrates biomedical knowledge with COVID-19 literature to generate drug repurposing hypotheses. This tool aids in identifying potential treatments by analyzing drug action pathways against COVID-19.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Drug Discovery
Background:
- The COVID-19 pandemic necessitated rapid development of therapeutic strategies.
- Existing biomedical knowledge graphs required integration with emerging pandemic-specific data.
Purpose of the Study:
- To develop a comprehensive knowledge base for COVID-19 research.
- To facilitate hypothesis generation for drug repurposing against COVID-19.
Main Methods:
- Integrated the Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) knowledge graph.
- Incorporated data from the CORD-19 collection, annotating recent biomedical literature on COVID-19.
- Developed confirmatory pathways to analyze drug action.
Main Results:
- Established COVID-KOP, a novel knowledge base for COVID-19.
- Demonstrated COVID-KOP's effectiveness in generating drug repurposing hypotheses.
- Identified potential drug candidates and repurposing strategies for COVID-19 treatment.
Conclusions:
- COVID-KOP serves as a valuable resource for accelerating COVID-19 drug discovery.
- The knowledge base supports the identification of therapeutic interventions through pathway analysis.
- COVID-KOP is accessible for public use to aid pandemic research.
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